Wearable Soft Technologies for Haptic Sensing and Feedback

Propulsion of chiral magnetic nanomotors powered by a rotating magnetic field is in the focus of the modern biomedical applications. This technology relies on strong interactions of dynamic and magnetic degrees of freedom of the system. Here we study in detail various experimentally observed regimes of the helical nanomotor orientation and propulsion depending on the actuation frequency, and establish the relation of these two properties to the remanent magnetization and geometry of the helical nanomotors. The theoretical predictions for the transition between the regimes and nanomotor orientation and propulsion speed are in excellent agreement with available experimental data. The proposed theory offers a few simple guidelines towards the optimal design of the magnetic nanomotors.

pdf/the-chiral-magnetic-nanomotors-z7911d4a7t.pdf

The chiral magnetic nanomotors

We present a non-contact manipulation method for micron scale objects using locally induced rotational fluid flows created by groups of untethered magnetic micro-manipulators. The magnetic micro-manipulators are rotated in a viscous fluid by an externally generated magnetic field to create rotational flows, which act to move micro-objects in the flow region. One single spherical micro-manipulator is used to manipulate one object at a time, while an array of micro-manipulators spin in synchrony on a surface patterned with magnetic micro-docks to create reconfigurable fluidic channels for simultaneous transportation of multiple objects. The induced rotational flow field and the resulting hydrodynamic forces on the micro-objects are studied using both finite element solutions and analytical models from previous studies. These results are compared with experiment to determine manipulation characteristics for the complex flows. Due to its untethered and non-contact operation, this micro-manipulation method cou...

Micro-manipulation using rotational fluid flows induced by remote magnetic micro-manipulators

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

: This paper explores the possibility of using multiplicative gate to build two recurrent neural network structures. These two structures are called Deep Simple Gated Unit (DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM, which require more than one gate to control information flow in the network, SGU and DSGU only use one multiplicative gate to control the flow of information. We show that this difference can accelerate the learning speed in tasks that require long dependency information. We also show that DSGU is more numerically stable than SGU. In addition, we also propose a standard way of representing the inner structure of RNN called RNN Conventional Graph (RCG), which helps to analyze the relationship between input units and hidden units of RNN.

/pdf/deep-gate-recurrent-neural-network-32itvmha7k.pdf

Deep Gate Recurrent Neural Network

/pdf/a-hybrid-estimation-framework-for-cooperative-localization-1f2c48yb0z.pdf

A hybrid estimation framework for cooperative localization under communication constraints

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A hybrid estimation framework for cooperative localization under communication constraints